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<a href="_epsilon_svm_trainer_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> * </span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       Trainer for the Epsilon-Support Vector Machine for Regression</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> * </span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> * </span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> * </span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> *</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \author      T. Glasmachers</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> * \date        2007-2012</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> *</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * </span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * </span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published </span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * </span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * </span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> *</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment"> */</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span> </div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_EPSILONSVMTRAINER_H</span></div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span><span class="preprocessor">#define SHARK_ALGORITHMS_EPSILONSVMTRAINER_H</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_svm_trainer_8h.html">shark/Algorithms/Trainers/AbstractSvmTrainer.h</a>&gt;</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;<a class="code" href="_svm_problems_8h.html">shark/Algorithms/QP/SvmProblems.h</a>&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span><span class="preprocessor">#include &lt;<a class="code" href="_block_matrix2x2_8h.html">shark/LinAlg/BlockMatrix2x2.h</a>&gt;</span></div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span><span class="preprocessor">#include &lt;<a class="code" href="_cached_matrix_8h.html">shark/LinAlg/CachedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_matrix_8h.html">shark/LinAlg/KernelMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="preprocessor">#include &lt;<a class="code" href="_precomputed_matrix_8h.html">shark/LinAlg/PrecomputedMatrix.h</a>&gt;</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span> </div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span> </div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment"></span> </div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">/// \brief Training of Epsilon-SVMs for regression.</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">/// The Epsilon-SVM is a support vector machine variant</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">/// for regression problems. Given are data tuples</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// \f$ (x_i, y_i) \f$ with x-component denoting input and</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">/// y-component denoting a real-valued label (see the tutorial on</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">/// label conventions; the implementation uses RealVector),</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">/// a kernel function k(x, x&#39;), a regularization constant C &gt; 0,</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">/// and a loss insensitivity parameter \f$ \varepsilon \f$.</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">/// Let H denote the kernel induced reproducing kernel Hilbert</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// space of k, and let \f$ \phi \f$ denote the corresponding</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span><span class="comment">/// feature map. Then the SVM regression function is of the form</span></div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="comment">///     (x) = \langle w, \phi(x) \rangle + b</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span><span class="comment">/// with coefficients w and b given by the (primal)</span></div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span><span class="comment">/// optimization problem</span></div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">///     \min \frac{1}{2} \|w\|^2 + C \sum_i L(y_i, f(x_i)),</span></div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="comment">/// where</span></div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span><span class="comment">/// \f[</span></div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span><span class="comment">///     L(y, f(x)) = \max\{0, |y - f(x)| - \varepsilon \}</span></div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment">/// \f]</span></div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">/// is the \f$ \varepsilon \f$ insensitive absolute loss.</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">///</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">/// \ingroup supervised_trainer</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment"></span><span class="keyword">template</span> &lt;<span class="keyword">class</span> InputType, <span class="keyword">class</span> CacheType = <span class="keywordtype">float</span>&gt;</div>
<div class="foldopen" id="foldopen00080" data-start="{" data-end="};">
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html">   80</a></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_epsilon_svm_trainer.html" title="Training of Epsilon-SVMs for regression.">EpsilonSvmTrainer</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer</a>&lt;InputType, RealVector, KernelExpansion&lt;InputType&gt; &gt;</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>{</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span> </div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a3016ebf54b283192dc08391f2160c194">   84</a></span>    <span class="keyword">typedef</span> CacheType <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#a3016ebf54b283192dc08391f2160c194">QpFloatType</a>;</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span> </div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a4f9bbb0afb8eff93970e7f51f11ee86c">   86</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_kernel_matrix.html" title="Kernel Gram matrix.">KernelMatrix&lt; InputType, QpFloatType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#a4f9bbb0afb8eff93970e7f51f11ee86c">KernelMatrixType</a>;</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#ae9818f09ebb48a69b1b28d3042da56da">   87</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_block_matrix2x2.html" title="SVM regression matrix.">BlockMatrix2x2&lt; KernelMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#ae9818f09ebb48a69b1b28d3042da56da">BlockMatrixType</a>;</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#ac3292f6302b431cb751edc7a4dcf6c74">   88</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_cached_matrix.html" title="Efficient quadratic matrix cache.">CachedMatrix&lt; BlockMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#ac3292f6302b431cb751edc7a4dcf6c74">CachedBlockMatrixType</a>;</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#aefaf6c666566d00fcd2822f6e3e34f46">   89</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_precomputed_matrix.html" title="Precomputed version of a matrix for quadratic programming.">PrecomputedMatrix&lt; BlockMatrixType &gt;</a> <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#aefaf6c666566d00fcd2822f6e3e34f46">PrecomputedBlockMatrixType</a>;</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span> </div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a8989b04b8588b9481f1ead75a26fae71">   91</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_model.html" title="Base class for all Models.">AbstractModel&lt;InputType, RealVector&gt;</a> <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#a8989b04b8588b9481f1ead75a26fae71">ModelType</a>;</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a5113162c6d0b5946ea23a303f6193682">   92</a></span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html" title="Base class of all Kernel functions.">AbstractKernelFunction&lt;InputType&gt;</a> <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#a5113162c6d0b5946ea23a303f6193682">KernelType</a>;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>    <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html" title="Super class of all kernelized (non-linear) SVM trainers.">AbstractSvmTrainer&lt;InputType, RealVector, KernelExpansion&lt;InputType&gt;</a> &gt; <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span><span class="comment"></span> </div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment">    /// Constructor</span></div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    /// \param  kernel         kernel function to use for training and prediction</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    /// \param  C              regularization parameter - always the &#39;true&#39; value of C, even when unconstrained is set</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    /// \param  epsilon        Loss insensitivity parameter.</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    //! \param  unconstrained  when a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?</span></div>
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a3854918319188d0e3dc58d594bf8bdb7">  100</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a3854918319188d0e3dc58d594bf8bdb7">EpsilonSvmTrainer</a>(<a class="code hl_class" href="classshark_1_1_abstract_kernel_function.html">KernelType</a>* <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a6cca95658d21c729fe2ccce525852756">epsilon</a>, <span class="keywordtype">bool</span> unconstrained = <span class="keyword">false</span>)</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    : <a class="code hl_class" href="classshark_1_1_abstract_svm_trainer.html">base_type</a>(<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a084595212c691b938fe6d421f40a908b">kernel</a>, <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>, true, unconstrained)</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>    , m_epsilon(<a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a6cca95658d21c729fe2ccce525852756">epsilon</a>)</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    { }</div>
</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span><span class="comment"></span> </div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00106" data-start="{" data-end="}">
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#ae4567615c33a2a3aec07382c3f2538d8">  106</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#ae4567615c33a2a3aec07382c3f2538d8" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> <span class="stringliteral">&quot;EpsilonSvmTrainer&quot;</span>; }</div>
</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span> </div>
<div class="foldopen" id="foldopen00109" data-start="{" data-end="}">
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a6cca95658d21c729fe2ccce525852756">  109</a></span>    <span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a6cca95658d21c729fe2ccce525852756">epsilon</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> m_epsilon; }</div>
</div>
<div class="foldopen" id="foldopen00111" data-start="{" data-end="}">
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#ab6bdf1036213a92736abb9930d56de5c">  111</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#ab6bdf1036213a92736abb9930d56de5c">setEpsilon</a>(<span class="keywordtype">double</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a6cca95658d21c729fe2ccce525852756">epsilon</a>)</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>    { m_epsilon = <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a6cca95658d21c729fe2ccce525852756">epsilon</a>; }</div>
</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span><span class="comment"></span> </div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span><span class="comment">    /// get the hyper-parameter vector</span></div>
<div class="foldopen" id="foldopen00115" data-start="{" data-end="}">
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a631651231f0bfbe6d61c26117cdb4c6b">  115</a></span><span class="comment"></span>    RealVector <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a631651231f0bfbe6d61c26117cdb4c6b" title="get the hyper-parameter vector">parameterVector</a>()<span class="keyword"> const</span>{</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        <span class="keywordtype">double</span> pEps = <a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa3e2f2db97947d244213f63093a08878" title="Is log(C) stored internally as a parameter instead of C? If yes, then we get rid of the constraint C ...">base_type::m_unconstrained</a> ? std::log(m_epsilon) : m_epsilon;</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        <span class="keywordflow">return</span> <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a183757faebfc331f6733946a6ea7de2c" title="get the hyper-parameter vector">base_type::parameterVector</a>() | pEps;</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>    }</div>
</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span><span class="comment"></span> </div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span><span class="comment">    /// set the vector of hyper-parameters</span></div>
<div class="foldopen" id="foldopen00121" data-start="{" data-end="}">
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a62ea3ab316e7985eb111d9b4b3172d64">  121</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a62ea3ab316e7985eb111d9b4b3172d64" title="set the vector of hyper-parameters">setParameterVector</a>(RealVector <span class="keyword">const</span>&amp; newParameters){</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        <span class="keywordtype">size_t</span> sp = <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#ab4f0632a6c463dca0103a38a6305c38c" title="return the number of hyper-parameters">base_type::numberOfParameters</a>();</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        <a class="code hl_define" href="_exception_8h.html#a73abb5049a0168d72a48e72dda41708b">SHARK_ASSERT</a>(newParameters.size() == sp + 1);</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        <a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#aecde2bab6daf3fa44b94438c7ba79a24" title="set the vector of hyper-parameters">base_type::setParameterVector</a>(subrange(newParameters, 0, sp));</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>        <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#ab6bdf1036213a92736abb9930d56de5c">setEpsilon</a>(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aa3e2f2db97947d244213f63093a08878" title="Is log(C) stored internally as a parameter instead of C? If yes, then we get rid of the constraint C ...">base_type::m_unconstrained</a> ? std::exp(newParameters(sp)) : newParameters(sp));</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>    }</div>
</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment"></span> </div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    /// return the number of hyper-parameters</span></div>
<div class="foldopen" id="foldopen00129" data-start="{" data-end="}">
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a20a2c523e7723a2b63cc9b459a865603">  129</a></span><span class="comment"></span>    <span class="keywordtype">size_t</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a20a2c523e7723a2b63cc9b459a865603" title="return the number of hyper-parameters">numberOfParameters</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="keyword">    </span>{ <span class="keywordflow">return</span> (<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#ab4f0632a6c463dca0103a38a6305c38c" title="return the number of hyper-parameters">base_type::numberOfParameters</a>() + 1); }</div>
</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span> </div>
<div class="foldopen" id="foldopen00132" data-start="{" data-end="}">
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno"><a class="line" href="classshark_1_1_epsilon_svm_trainer.html#a51d1821c3f6cedeff400e54f6e2b3b5d">  132</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_epsilon_svm_trainer.html#a51d1821c3f6cedeff400e54f6e2b3b5d">train</a>(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; svm, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, RealVector&gt;</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>        svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a38c97766f52bf00e5b0120c46c15f37f">setStructure</a>(<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>,dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>(),<span class="keyword">true</span>,1);</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>        </div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>        <a class="code hl_define" href="_exception_8h.html#adce1f80097c69010f5eab2618fa2e971">SHARK_RUNTIME_CHECK</a>(<a class="code hl_function" href="group__shark__globals.html#ga3006553139477e356ee75cd85c190d7c" title="Return the label/output dimensionality of a labeled dataset.">labelDimension</a>(dataset) == 1, <span class="stringliteral">&quot;Can only train 1D labels&quot;</span>);</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span> </div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#ae90c5c93fc02fad6fc07ca6b04fc78cc" title="Flag for using a precomputed kernel matrix.">QpConfig::precomputeKernel</a>())</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            trainSVM&lt;PrecomputedBlockMatrixType&gt;(svm,dataset);</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <span class="keywordflow">else</span></div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>            trainSVM&lt;CachedBlockMatrixType&gt;(svm,dataset);</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        </div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>        <span class="keywordflow">if</span> (<a class="code hl_function" href="classshark_1_1_qp_config.html#a32477b55142b80bd9f82f2a2e201f5b9" title="Flag for sparsifying the model after training.">base_type::sparsify</a>()) svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a503aaebca6ce5e7d8a6f79e5e039bd9f">sparsify</a>();</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>    }</div>
</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span> </div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span><span class="keyword">private</span>:</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>    <span class="keyword">template</span>&lt;<span class="keyword">class</span> MatrixType&gt;</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>    <span class="keywordtype">void</span> trainSVM(<a class="code hl_class" href="classshark_1_1_kernel_expansion.html" title="Linear model in a kernel feature space.">KernelExpansion&lt;InputType&gt;</a>&amp; svm, <a class="code hl_class" href="classshark_1_1_labeled_data.html" title="Data set for supervised learning.">LabeledData&lt;InputType, RealVector&gt;</a> <span class="keyword">const</span>&amp; dataset){</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_general_quadratic_problem.html" title="Quadratic Problem with only Box-Constraints Let K the kernel matrix, than the problem has the form.">GeneralQuadraticProblem&lt;MatrixType&gt;</a> SVMProblemType;</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>        <span class="keyword">typedef</span> <a class="code hl_class" href="classshark_1_1_svm_shrinking_problem.html">SvmShrinkingProblem&lt;SVMProblemType&gt;</a> ProblemType;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        </div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>        <span class="comment">//Set up the problem</span></div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#a4f9bbb0afb8eff93970e7f51f11ee86c">KernelMatrixType</a> km(*<a class="code hl_variable" href="classshark_1_1_abstract_svm_trainer.html#aec319e3ac1af74e75d5414624412dac3">base_type::m_kernel</a>, dataset.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>());</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        std::size_t ic = km.size();</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>        <a class="code hl_typedef" href="classshark_1_1_epsilon_svm_trainer.html#ae9818f09ebb48a69b1b28d3042da56da">BlockMatrixType</a> blockkm(&amp;km);</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        <a class="code hl_typedef" href="_mc_svm_linear_8cpp.html#a88ab98d46276376a56c2a396842cd58e">MatrixType</a> matrix(&amp;blockkm);</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>        SVMProblemType svmProblem(matrix);</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="keywordflow">for</span>(std::size_t i = 0; i != ic; ++i){</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>            svmProblem.linear(i) = dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(i).label(0) - m_epsilon;</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>            svmProblem.linear(i+ic) = dataset.<a class="code hl_function" href="group__shark__globals.html#gaec57b5f22b3e8d2d67ad4b621f30fd54">element</a>(i).label(0) + m_epsilon;</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            svmProblem.boxMin(i) = 0;</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            svmProblem.boxMax(i) = this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>();</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>            svmProblem.boxMin(i+ic) = -this-&gt;<a class="code hl_function" href="classshark_1_1_abstract_svm_trainer.html#a7bc3baa63458c785155a231ca73ea483" title="Return the value of the regularization parameter C.">C</a>();</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            svmProblem.boxMax(i+ic) = 0;</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        }</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        ProblemType problem(svmProblem,<a class="code hl_variable" href="classshark_1_1_qp_config.html#ac7bd118550c2bfa50f9497182b4b086d" title="should shrinking be used?">base_type::m_shrinking</a>);</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>        </div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <span class="comment">//solve it</span></div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>        QpSolver&lt; ProblemType&gt; solver(problem);</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        solver.solve(<a class="code hl_function" href="classshark_1_1_qp_config.html#a66fa342063f4fb0c8686a821dd14370e" title="Read/write access to the stopping condition.">base_type::stoppingCondition</a>(), &amp;<a class="code hl_function" href="classshark_1_1_qp_config.html#a0ea8552b2732cbfe664b7d0706c46d80" title="Access to the solution properties.">base_type::solutionProperties</a>());</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>        RealVector alpha = problem.getUnpermutedAlpha();</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        column(svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a3c65dfd17f38eaa461f6400d302fae48">alpha</a>(),0)= subrange(alpha,0,ic)+subrange(alpha,ic,2*ic);</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>        </div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>        <span class="comment">// compute the offset from the KKT conditions</span></div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        <span class="keywordtype">double</span> lowerBound = -1e100;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>        <span class="keywordtype">double</span> upperBound = 1e100;</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        <span class="keywordtype">double</span> sum = 0.0;</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>        </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>        std::size_t freeVars = 0;</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>        <span class="keywordflow">for</span> (std::size_t i=0; i&lt; ic; i++)</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        {</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            <span class="keywordflow">if</span> (problem.alpha(i) &gt; 0.0)</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>            {</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>                <span class="keywordtype">double</span> value = problem.gradient(i);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>                <span class="keywordflow">if</span> (problem.alpha(i) &lt; this-&gt;C())</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>                {</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>                    sum += value;</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>                    freeVars++;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>                }</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>                <span class="keywordflow">else</span></div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>                {</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>                    lowerBound = std::max(value,lowerBound);</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>                }</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span>            }</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>            <span class="keywordflow">if</span> (problem.alpha(i + ic) &lt; 0.0)</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            {</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>                <span class="keywordtype">double</span> value = problem.gradient(i + ic);</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>                <span class="keywordflow">if</span> (problem.alpha(i + ic) &gt; -this-&gt;C())</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>                {</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>                    sum += value;</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>                    freeVars++;</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>                }</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>                <span class="keywordflow">else</span></div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>                {</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>                    upperBound = std::min(value,upperBound);</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>                }</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>            }</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        }</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>        <span class="keywordflow">if</span> (freeVars &gt; 0) </div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>            svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>(0) = sum / freeVars;     <span class="comment">// stabilized (averaged) exact value</span></div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        <span class="keywordflow">else</span> </div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>            svm.<a class="code hl_function" href="classshark_1_1_kernel_expansion.html#a1c89cb50933ee211d67af90e6366e0ee">offset</a>(0) = 0.5 * (lowerBound + upperBound);    <span class="comment">// best estimate</span></div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        </div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        <a class="code hl_variable" href="classshark_1_1_qp_config.html#a073a19a266651c9a689f433b93ea4e3f" title="kernel access count">base_type::m_accessCount</a> = km.getAccessCount();</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>    }</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>    <span class="keywordtype">double</span> m_epsilon;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>};</div>
</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span> </div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span> </div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>}</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span><span class="preprocessor">#endif</span></div>
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